1. Field of the Invention
Many monitoring situations, both health and task-related, require continuous recording of vital signals in ambulatory subjects. This recording is performed in many different ways, such as with electrocardiography (ECG) and the like. For example, electrocardiographic testing during exercise remains the primary initial screening for coronary artery disease. Another diagnostic purpose of some ECG recordings is the detection of low amplitude signals occurring within the ST segment, known as late ventricular potentials (LVPs). Since these potentials may be precursors of sudden cardiac death, monitoring patients for extended periods is of diagnostic importance. Another application is the recording of heart rate for use as an indicator of the autonomic nervous system. In other applications, it is important to physiologically monitor healthy persons engaged in sensitive tasks such as aircraft piloting.
ECG signals contain information in a frequency band ranging from 0.05 Hz to at least 500 Hz. At the low frequency end, the most important portion of the ECG is the ST segment, the slope and height of which can indicate coronary perfusion. The high frequency end of the ECG spectrum may represent signals originating from discrete regions of the myocardium, the electrical conduction patterns of which are abnormal or distinct from the surrounding tissue.
In addition to the diagnostic information contained in specific frequency bands, there are also specific amplitude ranges that contain distinct diagnostic information. The low amplitude range, within approximately .+-.200 microvolts of the isoelectric line, contains information regarding acute ischemia, as recorded in the ST segment. Low amplitude signals also comprise the major portions of the P and T waves. Much of the diagnostic information from low amplitude signals is based on the simple presence or absence of the possible waves, and their direction with respect to the baseline. The precise shapes and sizes of the low amplitude waves are also important, and comprise the remainder of the information contained in the low amplitude signals. The highest amplitude signals of the ECG, that is, the R waves, are generally one log unit greater in amplitude than the low range, and represent the excitation of the bulk of the ventricle. The overall shape, size, and direction of the R wave provides information regarding the nature of ventricular excitation. Small amplitude signals that may be superimposed on the R wave may be of diagnostic value, but are generally not considered in standard ECG recordings.
For some diagnostic purposes, interval measurements are the primary goal. For example, heart rate variability measurements apply advanced mathematical analyses to a sequence of consecutive heart-beat intervals. The analysis depends on the sequential estimation of intervals between sinus-node activations, that correspond approximately to the occurrence of the P wave of the ECG. Since the P wave is often difficult to reliably detect, the much larger R wave is generally used as the fiducial marker of sinus node activation. The accuracy of R-R interval measurements is dependent on the method used to detect R waves. Relatively small errors in the measurement, especially if they are non-random, can be magnified by powerful mathematical analyses such as spectral analysis, leading to inaccurate diagnoses.
Another diagnostic purpose that relies heavily on interval measurement is the detection of late ventricular potentials (LVPs). The primary goal of this procedure is to detect the presence or absence of small high frequency signals following the QRS wave, and within the ST segment. Since LVPs have a signal-to-noise (S/N) ratio of less than one, averaging techniques are generally used to detect them. Since averaging must generally be used, it is important to accurately measure the occurrence of the R wave as a fiducial marker, and to measure the duration of the LVPs.
The ideal monitoring system should record and store the true signals as they change, within the entire relevant ranges of frequency and amplitude. Signal fidelity is thus of paramount importance, and it is the goal of all recording systems. However, practical recording systems, especially those that must continuously record from mobile subjects, all suffer technical limitations and therefore sacrifice some signal fidelity.
2. Description of the Prior Art
Until recently, the main options for continuous mobile recordings were either Holter monitors using magnetic tape, or radio-frequency telemetry. The disadvantages of the Holter method include its relative bulk, distortion from tape artifacts, inflexibility, and the necessity for reading and analyzing the tape using a specialized computer. Telemetry can be less bulky, but requires the proximity of a receiver-recorder.
The primary technical hurdle that must be overcome for silicon data recorders is adequate compression of data, without loss of significant information. Real-time continuous (24 hour) recording of signals on present-day silicon requires substantial data compression. For example, with 4 megabytes of silicon memory, an upper data rate of approximately 200 bits per second for each of 2 ECG channels can be stored. Since the sample rate recommended by the American Heart Association for ECG is 250 samples per second (8 bits per sample), a data compression factor of 10:1 would be required.
Several data compression techniques have been developed specifically for the ECG. These techniques are all based on mathematical algorithms that operate on data samples obtained at fixed sampling rates by a conventional analog to digital converter. Each data compression method can be quantified by two numbers, the compression ratio (CR) consisting of the number of incoming bits divided by the number of bits stored, and the percent root distortion (PRD), a measure of the amount of signal distortion caused by the compression. CR is monotonically related to PRD. Published values of CR range from 3 to 10, with PRD values ranging from 1 to 20, for various algorithms. Since the CR is relatively fixed for a given algorithm, a necessary strategy is to set the data sampling rate as low as possible in order to reduce the total amount of data.
Standard analog to digital conversion uses a signal level approximator which stores the current signal amplitude. This amplitude is read and stored at fixed intervals.
Data compression algorithms operate by identifying certain samples in the stored data array that are "redundant" and hence can be discarded. For electrocardiographic type signals, a sample is considered redundant if the possible amplitude values of the quantized signal are not equally probable. The redundant point need not be stored, since its value can be predicted (with some uncertainty) by the proximal points.
In general, the algorithms include two stages: compression and reconstruction. Compression is achieved by scanning each data sample and deciding, in comparison with adjacent samples using pre-defined criteria, whether or not the sample is redundant. The redundant points are thrown away without possibility of recovery, and thus the process is irreversible. The reconstruction stage uses a separate algorithm to interpolate samples back into the sample array, when it is being sent to an output device, such as a printer. The fidelity of the reconstructed signal is thus algorithm-dependent, and distortions are a necessary by-product. Algorithms differ with respect to the specific criteria used.
The amount of compression achieved (and the corresponding distortion) by a given algorithm can be controlled by parameter adjustment. It is important that the CR be sufficiently large to store the desired time epoch of the signal, and that the PRD is small enough to ensure adequate reconstruction. Since the algorithms operate from an automatic computer program, the CR and PRD values may not be entirely predictable, and therefore it is useful to monitor these values periodically during data collection. This is done by comparison of segments of reconstructed signals with the original data, while making necessary parameter adjustments.
A secondary type of data compression is used in some systems that involves beat classification. In this method, the waveform of each incoming cardiac cycle is compared with reference cycles, and if there are no significant differences, the beat is classified as the corresponding reference type. Thus, the only data stored for that cycle is the beat type and its time of occurrence.
Existing digital data storage apparatus use algorithms of the type listed above. They therefore suffer major disadvantages. One disadvantage is processing overhead, representing several instruction cycles per data point as data are either stored or discarded. Thus, considerable CPU time is devoted to the sampling and compressing of the data, while other real time processing tasks, such as analysis, are neglected. A second disadvantage is signal distortion, mainly in loss of high frequency signal components. This effect results from sampling rates that are set too low in order reduce the total data input rate prior to compression. For example, the commonly used sampling rate of 250 Hz necessitates anti-alias filtering with a cutoff of 125 Hz. Thus, potentially valuable higher frequency components are lost. A third disadvantage is that the algorithms require continual feedback and adjustment during data collection for effective operation. A fourth disadvantage is that standard compression methods will remove data points that are indistinguishable from noise in real time. Thus, the likelihood that small signals such as LVPs can be recorded with prior art is small.
Apparatus and methods related to the present invention are disclosed in U.S. Pat. No. 4,211,238 to Shu & Squires, U.S. Pat. No. 4,193,393, U.S. Pat. No. 4,799,165 to Hollister et al. and U.S. Pat. No. 4,624,263 to Slavin.